Sensor systems incorporating a plurality of sensors (multi-sensor systems) are widely used for a variety of military applications including ocean surveillance, air-to-air and surface-to-air defense (e.g., self-guided munitions), battlefield intelligence, surveillance and target detection (classification), and strategic warning and defense. Also, multi-sensor systems are used for a plurality of civilian applications including condition-based maintenance, robotics, automotive safety, remote sensing, weather forecasting, medical diagnoses, and environmental monitoring (e.g., weather forecasting).
To obtain the full advantage of a multi-sensor system, an efficient data fusion method (or architecture) may be selected to optimally combine the received data from the multiple sensors. For military applications (especially target recognition), a sensor-level fusion process is widely used wherein data received by each individual sensor is fully processed at each sensor before being output to a system data fusion processor. The data (signal) processing performed at each sensor may include a plurality of processing techniques to obtain desired system outputs (target reporting data) such as feature extraction, and target classification, identification, and tracking. The processing techniques may include time-domain, frequency-domain, multi-image pixel image processing techniques, and/or other techniques to obtain the desired target reporting data.
An exemplary, prior art example of a multi-sensor, sensor-level fusion (process) system 100 for automatic target recognition (ATR) is shown in FIG. 1. Advantageously, system 100 may include a plurality of sensors 102, 104, 106, 108 which may include RF sensors such as MMW radar (active sensor) 102, MMW radiometer (passive sensor) 104, IR laser radar 106, and passive IR sensor 108 (e.g., FLIR or IRST—infrared search and track). Additionally, multi-sensor system 100 may include data processor portion 118 which includes sensor parallel processor 120 and data fusion processor 122 which advantageously executes at least one predetermined algorithm to produce a valid target declaration output 124. Each sensor may scan a predetermined area (field of view) for an object (target) and receive data using antenna 110 (for the MMW sensors 102, 104) or lens 114, 116 (for IR sensors 106, 108). In accordance with the sensor-level fusion architecture selected, each sensor may have its individually received data processed (via parallel processor 120) using the predetermined algorithm that may be designed in accordance with a plurality of predetermined system parameters including received frequency band, active or passive operating mode of the individual sensor, sensor resolution and scanning characteristics, target and background signatures, and other predetermined system parameters. Results of the individual sensor processing may be input as a target report to the data fusion processor 122 (in response to a cue/query from the data fusion processor) where the results may be combined (fused) in accordance with the predetermined algorithm to produce an output decision 124 such as “validated target” or “no desired target encountered”. Other output decisions 124, such as tracking estimates, may be produced in accordance with multi-sensor system output requirements. The tracking estimates may be used to form new tracking results, update existing tracking, and estimate future positions of the object (target).
Many multi-sensor systems (such as system 100 in FIG. 1) use feature-level fusion wherein features that help discriminate (find small distinctions) among objects (targets) are extracted from each individual sensor's data and then combined to form a composite feature vector representative of the object in each sensor's field of view. The composite feature vector may be input to a data processor (or neural network) and classification (recognition of the object as a house, tank, truck, man, etc.) of the object may then occur using a predetermined algorithm (incorporating the previously described processing techniques) to recognize the object of interest, differentiate the object from decoys (false targets), and produce a weighted value or function (e.g., reliability value or function) that links the observed object to a particular (predetermined) target with some probability, confidence, threat priority, or other quantitative/qualitative system output.
Currently, a data fusion method (strategy) that is widely used for feature-level systems is multiplicative fusion (e.g., Bayes or Dempster-Shafer methods). Commonly, the multiplicative fusion method multiplies a plurality of probability functions (generated from the received data from each individual sensor) to produce a single term (value). The generation of the single term makes it complex to weight contributions from the plurality of sensors (which may have different reliability values over different tracking time periods due to different sensor constraints, atmospheric conditions, or other factors) and thus may produce a less accurate data fusion output (decision output regarding target classification). Additionally, under certain conditions, a data fusion reliability output (using data from all sensors) may be worse than a single sensor reliability output.
Therefore, due to the disadvantages of the current multiplicative data fusion method, there is a need to provide a multi-sensor system that uses an additive data fusion method to produce multiple terms for weighting and determines a current, better performing (higher reliability) sensor to adaptively weight the contributions from the plurality of different sensors for improved reliability of target classification. Also, there is a need to provide a multi-sensor data fusion system that can adaptively weight multi-sensor reliability towards the better performing sensor (and away from a worse performing sensor) when predetermined conditions arise making the better single sensor reliability higher than the data fusion (combining all sensor data) reliability.